Machine Learning Modeling for Spatial-Temporal Prediction of Geohazard
Geohazards, such as landslides, rock avalanches, debris flow, ground fissures, and ground subsidence, pose a significant threat to people’s lives and property. Recently, machine learning (ML) has become the predominant approach in geohazard modeling, offering advantages such as an excellent generali...
محفوظ في:
| التنسيق: | Online |
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| اللغة: | الإنجليزية |
| منشور في: |
MDPI - Multidisciplinary Digital Publishing Institute
2024
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| الموضوعات: | |
| الوصول للمادة أونلاين: | ONIX_20240108_9783036597867_146 |
| الوسوم: |
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| _version_ | 1863744127582076928 |
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| collection | Directory of Open Access Books |
| description | Geohazards, such as landslides, rock avalanches, debris flow, ground fissures, and ground subsidence, pose a significant threat to people’s lives and property. Recently, machine learning (ML) has become the predominant approach in geohazard modeling, offering advantages such as an excellent generalization ability and accurately describing complex and nonlinear behaviors. However, the utilization of advanced algorithms in deep learning remains poorly understood in this field. Additionally, there are fundamental challenges associated with ML modeling, including input variable selection, uncertainty quantification, and hyperparameter tuning. This reprint presents original research exploring new advances and challenges in the application of ML in the spatial–temporal modeling of geohazards. The contributions cover the susceptibility analysis of glacier debris flow and landslides, the displacement prediction of reservoir landslides, slope stability prediction and classification, building resilience evaluation, and the prediction of rainfall-induced landslide warning signals. |
| format | Online |
| id | doab-20.500.12854ir-132487 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1324872024-03-27T16:34:42Z Machine Learning Modeling for Spatial-Temporal Prediction of Geohazard Ma, Junwei Dou, Jie Geohazard modeling Spatial&ndash temporal prediction Machine learning thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general Geohazards, such as landslides, rock avalanches, debris flow, ground fissures, and ground subsidence, pose a significant threat to people’s lives and property. Recently, machine learning (ML) has become the predominant approach in geohazard modeling, offering advantages such as an excellent generalization ability and accurately describing complex and nonlinear behaviors. However, the utilization of advanced algorithms in deep learning remains poorly understood in this field. Additionally, there are fundamental challenges associated with ML modeling, including input variable selection, uncertainty quantification, and hyperparameter tuning. This reprint presents original research exploring new advances and challenges in the application of ML in the spatial–temporal modeling of geohazards. The contributions cover the susceptibility analysis of glacier debris flow and landslides, the displacement prediction of reservoir landslides, slope stability prediction and classification, building resilience evaluation, and the prediction of rainfall-induced landslide warning signals. 2024-01-08T14:59:15Z 2024-01-08T14:59:15Z 2023 book ONIX_20240108_9783036597867_146 9783036597867 9783036597874 https://directory.doabooks.org/handle/20.500.12854/132487 eng application/octet-stream Attribution 4.0 International https://mdpi.com/books/pdfview/book/8543 https://mdpi.com/books/pdfview/book/8543 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-0365-9787-4 10.3390/books978-3-0365-9787-4 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783036597867 9783036597874 274 open access |
| spellingShingle | Geohazard modeling Spatial&ndash temporal prediction Machine learning thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general Machine Learning Modeling for Spatial-Temporal Prediction of Geohazard |
| title | Machine Learning Modeling for Spatial-Temporal Prediction of Geohazard |
| title_full | Machine Learning Modeling for Spatial-Temporal Prediction of Geohazard |
| title_fullStr | Machine Learning Modeling for Spatial-Temporal Prediction of Geohazard |
| title_full_unstemmed | Machine Learning Modeling for Spatial-Temporal Prediction of Geohazard |
| title_short | Machine Learning Modeling for Spatial-Temporal Prediction of Geohazard |
| title_sort | machine learning modeling for spatial temporal prediction of geohazard |
| topic | Geohazard modeling Spatial&ndash temporal prediction Machine learning thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general |
| topic_facet | Geohazard modeling Spatial&ndash temporal prediction Machine learning thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general |
| url | ONIX_20240108_9783036597867_146 |